1
Mining Engineering, Kashan University Campus, Iran.
2
Faculty of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Abstract
In order to improve the geomechanical parameters and bearing capacity of the bedrock mass or to reduce permeability and improve the bed conditions of dams and engineering structures related to the earth, cement grout injection is performed. The injection pressure is a determining parameter in the success of the operation, such that low or high values of the grout injection pressure cause financial and time losses and project failure. Considering the wide range of parameters affecting the injection pressure, determining the optimal injection pressure using previous analytical and experimental relationships faces a large error, but in this article, with the help of the SVR method and using information from large and successful projects, the optimal injection pressure was determined with an acceptable R error rate (above 0.90). Also, by finding the optimal combination of input information using the GA-GT genetic algorithm and eliminating deviant data, the calculation error rate is minimized and the R error is improved by up to 30%. The SVR method finds and presents the best value for the dependent parameter in the space of independent parameters using different kernel functions.
Moosavi,S. E. and Bakhshandeh Amnieh,H. (2022). Investigating the performance of data-driven models in determining cement slurry pressure using a combined gamma test and genetic algorithm (GA-GT) method. JOURNAL OF ROCK MECHANICS, 5(4), 29-38.
MLA
Moosavi,S. E. , and Bakhshandeh Amnieh,H. . "Investigating the performance of data-driven models in determining cement slurry pressure using a combined gamma test and genetic algorithm (GA-GT) method", JOURNAL OF ROCK MECHANICS, 5, 4, 2022, 29-38.
HARVARD
Moosavi S. E., Bakhshandeh Amnieh H. (2022). 'Investigating the performance of data-driven models in determining cement slurry pressure using a combined gamma test and genetic algorithm (GA-GT) method', JOURNAL OF ROCK MECHANICS, 5(4), pp. 29-38.
CHICAGO
S. E. Moosavi and H. Bakhshandeh Amnieh, "Investigating the performance of data-driven models in determining cement slurry pressure using a combined gamma test and genetic algorithm (GA-GT) method," JOURNAL OF ROCK MECHANICS, 5 4 (2022): 29-38,
VANCOUVER
Moosavi S. E., Bakhshandeh Amnieh H. Investigating the performance of data-driven models in determining cement slurry pressure using a combined gamma test and genetic algorithm (GA-GT) method. JOURNAL OF ROCK MECHANICS, 2022; 5(4): 29-38.
